COVID-19 sürecinde uzaktan eğitime yönelik akademisyenlerin kullanıcı dirençlerinin teknoloji kabul modeli ile analiz edilmesi
Yıl 2022,
, 373 - 392, 31.12.2022
Fevziye Bekar
,
Handan Çam
Öz
Bu çalışmanın amacı COVID-19 sürecinde akademisyenlerin uzaktan eğitime karşı kullanıcı dirençlerini, uzaktan eğitim sistemlerine ilişkin algılarını, eğitim sisteminde meydana gelen değişikliklere yönelik algılarını etkileyen faktörlerin belirlenmesidir. Araştırmanın örneklemini Türkiye’de 43 farklı üniversitede görev yapan 440 akademisyen oluşturmaktadır. Araştırmanın örneklemi kolayda ve kartopu örneklem yöntemleriyle belirlenmiştir. Veriler Google Forms’da oluşturulan çevrimiçi anket formu kullanılarak elde edilmiştir. Veriler üzerinde geçerlilik ve güvenirlik analizleri yapıldıktan sonra yapısal eşitlik modeli kullanılarak 8 farklı hipotez test edilmiştir. Analiz sonucunda çalışma kapsamında önerilen sekiz hipotezin tümü kabul edilmiştir. Yürütülen çalışma kapsamında, değişime karşı direncin uzaktan eğitim sistemi kullanıcılarını önemli ölçüde olumsuz bir şekilde etkilediği bulgulanmıştır. Bununla birlikte, algılanan kullanım kolaylılığı ve faydanın ise tutum ve davranışlar üzerinde olumlu bir etkisi bulunmaktadır
Kaynakça
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- Aguilera-Hermida, A.P., Quiroga-Garza, A., Gómez-Mendoza, S., Villanueva, C.A.D.R., Alecchi, B.A., and Avci, D. (2021). Comparison of students’ use and acceptance of emergency online learning due to Covid-19 in the USA, Mexico, Peru, and Turkey. Education and Information Technologies, 1-23. doi:10.1007/s10639-021-10473-8
- Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., and Salloum, S. (2021). Using machine learning algorithms to predict people’s intentiont to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. Jmir Medical Education 7(1), 1-17, E24032. doi: 10.2196/24032
- Alam, M. (2020). Organisational processes and COVID-19 pandemic: implications for job design. Journal Of Accounting & Organizational Change, 16(4), 599-606. doi:10.1108/JAOC-08-2020-0121
- Alexandrakis, D., Chorianopoulos, K., and Tselios, N. (2020). Older adults and web 2.0 storytelling technologies: probing the technology acceptance model through an age-related perspective. International Journal of Human–Computer Interaction, 36, 1623-1635. doi:10.1080/10447318.2020.1768673
- Alfadda, H.A. and Mahdi, H.S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50, 883-900. doi:10.1007/s10936-020-09752-1
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- Alshurafat, H., Al Shbail, M.O., Masadeh, W.M., Dahmash, F., and Al-Msiedeen, J.M., (2021). Factors affecting online accounting education during the COVID-19 pandemic: An integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 1-19. doi:10.1007/s10639-021-10550-y
- Ambarwati, M.F.L. (2021). Technology use analysis for administrative assistants by using the theory of technology acceptance model. Jurnal Administrasi Dan Kesekretarisan, 6(1), 78-90. doi:10.36914/jak.v6i1.565
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- Aryana, B. and Clemmensen, T. (2013). Mobile Usability: Experiences from Iran and Turkey. International Journal of Human-Computer Interaction, 29, 220-242. doi:10.1080/10447318.2013.765760
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Analysing user resistance to distance learning systems by academics within the Covid-19 pandemic using the technology acceptance model
Yıl 2022,
, 373 - 392, 31.12.2022
Fevziye Bekar
,
Handan Çam
Öz
The aim of this study is to determine academics’ user resistance to distance learning, their perceptions of the distance learning systems and factors affecting their perceptions of the changes in the education system during the COVID-19 pandemic. The study’s population consists of 440 academics working in 43 different universities in Turkey. The research sample were determined through convenience and snowball sampling methods. The data were collected using an online questionnaire form created within Google Forms. After validity and reliability analyses on the data, eight different hypotheses were tested using structural equation analysis. All eight hypotheses proposed within the study were accepted after receiving the results of this analysis. The results of the study show that user resistance has a significantly negative effect on the users that utilise distance learning. However, the perceived ease of use and usefulness have a significantly positive effect on attitude and behavior.
Kaynakça
- Aguilera-Hermida, A.P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open 1, (100011), 1-8. doi:10.1016/j.ijedro.2020.100011
- Aguilera-Hermida, A.P., Quiroga-Garza, A., Gómez-Mendoza, S., Villanueva, C.A.D.R., Alecchi, B.A., and Avci, D. (2021). Comparison of students’ use and acceptance of emergency online learning due to Covid-19 in the USA, Mexico, Peru, and Turkey. Education and Information Technologies, 1-23. doi:10.1007/s10639-021-10473-8
- Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., and Salloum, S. (2021). Using machine learning algorithms to predict people’s intentiont to use mobile learning platforms during the COVID-19 pandemic: Machine learning approach. Jmir Medical Education 7(1), 1-17, E24032. doi: 10.2196/24032
- Alam, M. (2020). Organisational processes and COVID-19 pandemic: implications for job design. Journal Of Accounting & Organizational Change, 16(4), 599-606. doi:10.1108/JAOC-08-2020-0121
- Alexandrakis, D., Chorianopoulos, K., and Tselios, N. (2020). Older adults and web 2.0 storytelling technologies: probing the technology acceptance model through an age-related perspective. International Journal of Human–Computer Interaction, 36, 1623-1635. doi:10.1080/10447318.2020.1768673
- Alfadda, H.A. and Mahdi, H.S. (2021). Measuring students’ use of zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50, 883-900. doi:10.1007/s10936-020-09752-1
- Alhumaid, K., Ali, S., Waheed, A., Zahid, E., and Habes, M. (2020). COVID-19 and E-learning: perceptions and attitudes of teachers towards e-learning acceptance in the developing countries. Multicultural Education, 6(2), 100-115. doi: 10.5281/zenodo.4060121
- Almaiah, M.A., Al-Khasawneh, A., and Althunibat, A. (2020). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280. doi:10.1007/s10639-020-10219-y
- Alshurafat, H., Al Shbail, M.O., Masadeh, W.M., Dahmash, F., and Al-Msiedeen, J.M., (2021). Factors affecting online accounting education during the COVID-19 pandemic: An integrated perspective of social capital theory, the theory of reasoned action and the technology acceptance model. Education and Information Technologies, 1-19. doi:10.1007/s10639-021-10550-y
- Ambarwati, M.F.L. (2021). Technology use analysis for administrative assistants by using the theory of technology acceptance model. Jurnal Administrasi Dan Kesekretarisan, 6(1), 78-90. doi:10.36914/jak.v6i1.565
- Amoako-Gyampah, K. and Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. doi:10.1016/j.im.2003.08.010
- Aryana, B. and Clemmensen, T. (2013). Mobile Usability: Experiences from Iran and Turkey. International Journal of Human-Computer Interaction, 29, 220-242. doi:10.1080/10447318.2013.765760
- Asan, O. and Carayon, P. (2017). Human factors of health information technology—challenges and opportunities. International Journal of Human–Computer Interaction, 33(4), 255–257. doi:10.1080/10447318.2017.128275
- Asghar, M.Z., Barberà, E., and Younas, I. (2021). Mobile learning technology readiness and acceptance among pre-service teachers in Pakistan during the COVID-19 pandemic. Knowledge Management & E-Learning: An International Journal, 13(1), 83-101. doi:10.34105/j.kmel.2021.13.005
- Baber, H. (2021). Modelling the acceptance of e-learning during the pandemic of COVID-19-a study of South Korea. The International Journal of Management Education, 19, 1-15, 100503. doi:10.1016/j.ijme.2021.100503
- Basyal, D.K. and Seo, J.-W. (2017). Employees’ resistance to change and technology acceptance in Nepal. The Journal of University Grants Commission, 6, 1-15. Retrieved from: http://journals.pu.edu.pk/journals/index.php/IJSAS/article/view/3114
- Baş, T. (2008). Anket nasıl hazırlanır nasıl uygulanır nasıl değerlendirilir? Ankara: Seçkin Press.
- Bozpolat, C. and Seyhan, H. (2020). Mobil ödeme teknolojisi kabulünün teknoloji kabul modeli ile incelenmesi: Ampirik bir araştırma. Çankırı Karatekin Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(1), 119-145. doi:10.18074/ckuiibfd.619852
- Briz-Ponce, L., Pereira, A., Carvalho, L., Juanes-Méndez, J.A., and García-Peñalvo, F.J. (2017). Learning with mobile technologies–students’ behavior. Computers in Human Behavior, 72, 612-620. doi:10.1016/j.chb.2016.05.027
- Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York: Guilford Publications.
- Büyüköztürk, Ş. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, 32(32), 470-483. Retrieved from: https://dergipark.org.tr/en/pub/kuey/issue/10365/126871
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